{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型安全\n",
    "\n",
    "## 概述\n",
    "\n",
    "本次体验流程介绍MindArmour提供的模型安全防护手段，引导您快速使用MindArmour，为您的AI模型提供一定的安全防护能力。\n",
    "\n",
    "AI算法设计之初普遍未考虑相关的安全威胁，使得AI算法的判断结果容易被恶意攻击者影响，导致AI系统判断失准。攻击者在原始样本处加入人类不易察觉的微小扰动，导致深度学习模型误判，称为对抗样本攻击。MindArmour模型安全提供对抗样本生成、对抗样本检测、模型防御、攻防效果评估等功能，为AI模型安全研究和AI应用安全提供重要支撑。\n",
    "\n",
    "- 对抗样本生成模块支持安全工程师快速高效地生成对抗样本，用于攻击AI模型。\n",
    "- 对抗样本检测、防御模块支持用户检测过滤对抗样本、增强AI模型对于对抗样本的鲁棒性。\n",
    "- 评估模块提供多种指标全面评估对抗样本攻防性能。\n",
    "\n",
    "接下来通过图像分类任务上的对抗性攻防，以攻击算法FGSM和防御算法NAD为例，体验MindArmour在对抗攻防上的使用方法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 整体流程\n",
    "\n",
    "1. 准备环节。下载MNIST数据集，配置运行信息和数据处理。\n",
    "2. 预训练模型。定义LeNet5网络，训练模型，生成CheckPoint文件。\n",
    "3. 建立被攻击模型。加载预训练模型，测试模型精度，攻击模型，测试攻击后模型精度。\n",
    "4. 对抗性防御。防御实现和防御效果分析。\n",
    "\n",
    "> 本次体验流程支持硬件平台为：\n",
    "> - CPU：在配置运行信息环节配置`context.set_context`中的`device_target`参数为`device_target=\"CPU\"`（本次体验默认使用CPU硬件平台）。\n",
    "> - GPU：在配置运行信息环节配置`context.set_context`中的`device_target`参数为`device_target=\"GPU\"`。\n",
    "> - Ascend：在配置运行信息环节配置`context.set_context`中的`device_target`参数为`device_target=\"Ascend\"`。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备环节\n",
    "\n",
    "###  下载数据集\n",
    "\n",
    "本次体验使用MNIST数据集，定义函数`download_dataset`下载MNIST数据集到当前工作目录下的`./MNIST_Data/`目录里。或者可以将下载好的训练和测试数据集解压后分别放在当前工作目录下的`./MNIST_Data/train`、`./MNIST_Data/test`路径下。\n",
    "\n",
    "> MNIST数据集下载地址为：<http://yann.lecun.com/exdb/mnist/>，页面提供4个数据集下载链接，其中前2个文件是训练数据需要，后2个文件是测试结果需要。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*****Downloading the MNIST dataset******\n",
      "\n",
      "***************Downloaded***************\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import urllib.request\n",
    "from urllib.parse import urlparse\n",
    "import gzip\n",
    "\n",
    "\n",
    "def unzip_file(gzip_path):\n",
    "    \"\"\"\n",
    "    unzip dataset file\n",
    "    \n",
    "    Args:\n",
    "        gzip_path (str): Dataset file path.\n",
    "    \"\"\"\n",
    "    open_file = open(gzip_path.replace('.gz',''), 'wb')\n",
    "    gz_file = gzip.GzipFile(gzip_path)\n",
    "    open_file.write(gz_file.read())\n",
    "    gz_file.close()\n",
    "\n",
    "def download_dataset():\n",
    "    \"\"\"Download the dataset from http://yann.lecun.com/exdb/mnist/.\"\"\"\n",
    "    print(\"{:*^40}\".format(\"Downloading the MNIST dataset\"))\n",
    "    train_path = \"./MNIST_Data/train/\"\n",
    "    test_path = \"./MNIST_Data/test/\"\n",
    "    train_path_check = os.path.exists(train_path)\n",
    "    test_path_check = os.path.exists(test_path)\n",
    "    if train_path_check == False and test_path_check ==False:\n",
    "        os.makedirs(train_path)\n",
    "        os.makedirs(test_path)\n",
    "    train_url = {\"http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\", \"http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\"}\n",
    "    test_url = {\"http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\", \"http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\"}\n",
    "    for url in train_url:\n",
    "        url_parse = urlparse(url)\n",
    "        # split the file name from url\n",
    "        file_name = os.path.join(train_path,url_parse.path.split('/')[-1])\n",
    "        if not os.path.exists(file_name.replace('.gz','')):\n",
    "            file = urllib.request.urlretrieve(url, file_name)\n",
    "            unzip_file(file_name)\n",
    "            os.remove(file_name)\n",
    "    for url in test_url:\n",
    "        url_parse = urlparse(url)\n",
    "        # split the file name from url\n",
    "        file_name = os.path.join(test_path,url_parse.path.split('/')[-1])\n",
    "        if not os.path.exists(file_name.replace('.gz','')):\n",
    "            file = urllib.request.urlretrieve(url, file_name)\n",
    "            unzip_file(file_name)\n",
    "            os.remove(file_name)\n",
    "    print(\"\\n{:*^40}\".format(\"Downloaded\"))\n",
    "\n",
    "download_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MNIST数据集下载完成后，此时当前工作目录下`MNIST`目录结构为：\n",
    "\n",
    "```shell\n",
    "MNIST_Data\n",
    "├── test\n",
    "│   ├── t10k-images-idx3-ubyte\n",
    "│   └── t10k-labels-idx1-ubyte\n",
    "└── train\n",
    "    ├── train-images-idx3-ubyte\n",
    "    └── train-labels-idx1-ubyte\n",
    "```\n",
    "\n",
    "- 其中：\n",
    "    - `t10k-images-idx3-ubyte`为测试图像数据文件。\n",
    "    - `t10k-labels-idx1-ubyte`为测试图像标签文件。\n",
    "    - `train-images-idx3-ubyte`为训练图像数据文件。\n",
    "    - `train-labels-idx1-ubyte`为训练图像标签文件。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 配置运行信息\n",
    "\n",
    "\n",
    "运行以下一段代码配置运行环境。其中：\n",
    "- `device_target`：指定运行环境，本次体验流程基于CPU环境，配置`device_target=\"CPU\"`。\n",
    "- `set_level`：指定LOGGER输出等级，此处配置为`LOGGER.set_level(1)`。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mindspore import context\n",
    "from mindarmour.utils.logger import LogUtil\n",
    "\n",
    "\n",
    "context.set_context(mode=context.GRAPH_MODE, device_target=\"CPU\")\n",
    "LOGGER = LogUtil.get_instance()\n",
    "LOGGER.set_level(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据处理\n",
    "\n",
    "利用MindSpore的`dataset`模块提供的`MnistDataset`接口加载MNIST数据集，定义函数`generate_mnist_dataset`对原始数据进行预处理操作，以创建可用于训练和测试的数据集。利用数据加载函数`generate_mnist_dataset`载入数据生成训练数据集`ds_train`和测试数据集`ds_test`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mindspore.dataset as ds\n",
    "import mindspore.dataset.transforms.vision.c_transforms as CV\n",
    "import mindspore.dataset.transforms.c_transforms as C\n",
    "from mindspore.dataset.transforms.vision import Inter\n",
    "from mindspore.common import dtype as mstype\n",
    "\n",
    "\n",
    "# generate testing data\n",
    "def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1,\n",
    "                           num_parallel_workers=1, sparse=True):\n",
    "    \"\"\"\n",
    "    create dataset for training or testing\n",
    "    \"\"\"\n",
    "    # define dataset\n",
    "    ds1 = ds.MnistDataset(data_path)\n",
    "\n",
    "    # define operation parameters\n",
    "    resize_height, resize_width = 32, 32\n",
    "    rescale = 1.0 / 255.0\n",
    "    shift = 0.0\n",
    "\n",
    "    # define map operations\n",
    "    resize_op = CV.Resize((resize_height, resize_width),\n",
    "                          interpolation=Inter.LINEAR)\n",
    "    rescale_op = CV.Rescale(rescale, shift)\n",
    "    hwc2chw_op = CV.HWC2CHW()\n",
    "    type_cast_op = C.TypeCast(mstype.int32)\n",
    "\n",
    "    # apply map operations on images\n",
    "    if not sparse:\n",
    "        one_hot_enco = C.OneHot(10)\n",
    "        ds1 = ds1.map(input_columns=\"label\", operations=one_hot_enco,\n",
    "                      num_parallel_workers=num_parallel_workers)\n",
    "        type_cast_op = C.TypeCast(mstype.float32)\n",
    "    ds1 = ds1.map(input_columns=\"label\", operations=type_cast_op,\n",
    "                  num_parallel_workers=num_parallel_workers)\n",
    "    ds1 = ds1.map(input_columns=\"image\", operations=resize_op,\n",
    "                  num_parallel_workers=num_parallel_workers)\n",
    "    ds1 = ds1.map(input_columns=\"image\", operations=rescale_op,\n",
    "                  num_parallel_workers=num_parallel_workers)\n",
    "    ds1 = ds1.map(input_columns=\"image\", operations=hwc2chw_op,\n",
    "                  num_parallel_workers=num_parallel_workers)\n",
    "\n",
    "    # apply DatasetOps\n",
    "    buffer_size = 10000\n",
    "    ds1 = ds1.shuffle(buffer_size=buffer_size)\n",
    "    ds1 = ds1.batch(batch_size, drop_remainder=True)\n",
    "    ds1 = ds1.repeat(repeat_size)\n",
    "\n",
    "    return ds1\n",
    "\n",
    "batch_size = 32\n",
    "ds_train = generate_mnist_dataset(\"./MNIST_Data/train/\")\n",
    "ds_test = generate_mnist_dataset(\"./MNIST_Data/test/\", batch_size=batch_size, sparse=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&emsp;&emsp;运行以下一段代码，打印数据集`ds_train`第一组共32张训练图像信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 32 images with label of the first batch in ds_train are showed below:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "print(\"The 32 images with label of the first batch in ds_train are showed below:\")\n",
    "ds_iterator = ds_train.create_dict_iterator()\n",
    "ds_iterator.get_next()\n",
    "batch_1 = ds_iterator.get_next()\n",
    "batch_image = batch_1[\"image\"]\n",
    "batch_label = batch_1[\"label\"]\n",
    "%matplotlib inline\n",
    "plt.figure(dpi=144)\n",
    "for i,image in enumerate(batch_image):\n",
    "    plt.subplot(4, 8, i+1)\n",
    "    plt.subplots_adjust(wspace=0.2, hspace=0.2)\n",
    "    image = np.squeeze(image, 0)\n",
    "    image = image/np.amax(image)\n",
    "    image = np.clip(image, 0, 1)\n",
    "    plt.imshow(image, cmap=\"gray\")\n",
    "    plt.title(f\"image {i+1}\\nlabel:  {batch_label[i]}\", y=-0.65, fontdict={\"fontsize\":8})\n",
    "    plt.axis('off')    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预训练模型\n",
    "\n",
    "### 定义网络\n",
    "\n",
    "本次流程以LeNet5模型为例，您也可以建立训练自己的模型。运行以下一段代码，定义LeNet5网络模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mindspore.nn as nn\n",
    "from mindspore.common.initializer import TruncatedNormal\n",
    "\n",
    "\n",
    "def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):\n",
    "    weight = weight_variable()\n",
    "    return nn.Conv2d(in_channels, out_channels,\n",
    "                     kernel_size=kernel_size, stride=stride, padding=padding,\n",
    "                     weight_init=weight, has_bias=False, pad_mode=\"valid\")\n",
    "\n",
    "\n",
    "def fc_with_initialize(input_channels, out_channels):\n",
    "    weight = weight_variable()\n",
    "    bias = weight_variable()\n",
    "    return nn.Dense(input_channels, out_channels, weight, bias)\n",
    "\n",
    "\n",
    "def weight_variable():\n",
    "    return TruncatedNormal(0.02)\n",
    "\n",
    "\n",
    "class LeNet5(nn.Cell):\n",
    "    \"\"\"\n",
    "    Lenet network\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        super(LeNet5, self).__init__()\n",
    "        self.conv1 = conv(1, 6, 5)\n",
    "        self.conv2 = conv(6, 16, 5)\n",
    "        self.fc1 = fc_with_initialize(16*5*5, 120)\n",
    "        self.fc2 = fc_with_initialize(120, 84)\n",
    "        self.fc3 = fc_with_initialize(84, 10)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)\n",
    "        self.flatten = nn.Flatten()\n",
    "\n",
    "    def construct(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.max_pool2d(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.max_pool2d(x)\n",
    "        x = self.flatten(x)\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型\n",
    "\n",
    "运行以下一段代码，训练模型并保存CheckPoint文件。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============== Starting Training ==============\n",
      "epoch: 1 step: 1, loss is 2.2966673\n",
      "epoch: 1 step: 2, loss is 2.3142211\n",
      "epoch: 1 step: 3, loss is 2.3002968\n",
      "epoch: 1 step: 4, loss is 2.2986352\n",
      "epoch: 1 step: 5, loss is 2.3062992\n",
      "epoch: 1 step: 6, loss is 2.3027751\n",
      "epoch: 1 step: 7, loss is 2.3007572\n",
      "epoch: 1 step: 8, loss is 2.3042161\n",
      "epoch: 1 step: 9, loss is 2.2996876\n",
      "epoch: 1 step: 10, loss is 2.3026795\n",
      "\n",
      "...\n",
      "\n",
      "epoch: 10 step: 1865, loss is 0.051650025\n",
      "epoch: 10 step: 1866, loss is 0.004669161\n",
      "epoch: 10 step: 1867, loss is 0.00537819\n",
      "epoch: 10 step: 1868, loss is 0.0038884382\n",
      "epoch: 10 step: 1869, loss is 0.05690967\n",
      "epoch: 10 step: 1870, loss is 0.08228033\n",
      "epoch: 10 step: 1871, loss is 0.00020364714\n",
      "epoch: 10 step: 1872, loss is 0.065785594\n",
      "epoch: 10 step: 1873, loss is 0.028175712\n",
      "epoch: 10 step: 1874, loss is 0.00413411\n",
      "epoch: 10 step: 1875, loss is 0.007664079\n",
      "Epoch time: 28778.540, per step time: 15.349\n"
     ]
    }
   ],
   "source": [
    "from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor\n",
    "from mindspore.train import Model\n",
    "from mindspore.nn.metrics import Accuracy\n",
    "\n",
    "\n",
    "lr = 0.01\n",
    "momentum = 0.9\n",
    "network = LeNet5()\n",
    "net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction=\"mean\")\n",
    "net_opt = nn.Momentum(network.trainable_params(), lr, momentum)\n",
    "time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())\n",
    "config_ck = CheckpointConfig(save_checkpoint_steps=1875,\n",
    "                             keep_checkpoint_max=10)\n",
    "ckpoint_cb = ModelCheckpoint(prefix=\"checkpoint_lenet\", config=config_ck)\n",
    "model = Model(network, net_loss, net_opt, metrics={\"Accuracy\": Accuracy()})\n",
    "\n",
    "print(\"============== Starting Training ==============\")\n",
    "model.train(epoch=10, train_dataset=ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()],\n",
    "                dataset_sink_mode=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建立被攻击模型\n",
    "\n",
    "以MNIST为示范数据集，自定义的简单模型LeNet5网络模型作为被攻击模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载LeNet模型文件\n",
    "\n",
    "运行以下一段代码，加载预训练的LeNet模型文件（`checkpoint_lenet-10_1875.ckpt`）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from mindspore.train.serialization import load_checkpoint, load_param_into_net\n",
    "\n",
    "\n",
    "ckpt_name = './checkpoint_lenet-10_1875.ckpt'\n",
    "net = LeNet5()\n",
    "load_dict = load_checkpoint(ckpt_name)\n",
    "load_param_into_net(net, load_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试攻击前模型精度\n",
    "\n",
    "从训练数据集`ds_test`中抽取前3组共96张数据图像和标签用于测试预训练模型精度。运行以下一段代码，测试预训练模型对被攻击之前的测试图像进行预测的精度。通过输出INFO信息可以看到，测试结果中分类精度达到了97.9%。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:24,108 [<ipython-input-8-5a5e4376f509>:26] [demo] prediction accuracy before attacking is : 0.9791666666666666\n"
     ]
    }
   ],
   "source": [
    "from mindspore import Tensor\n",
    "\n",
    "\n",
    "# prediction accuracy before attack\n",
    "TAG = 'demo'\n",
    "model = Model(net)\n",
    "batch_num = 3  # the number of batches of attacking samples\n",
    "test_images = []\n",
    "test_labels = []\n",
    "predict_labels = []\n",
    "i = 0\n",
    "for data in ds_test.create_tuple_iterator():\n",
    "    i += 1\n",
    "    images = data[0].astype(np.float32)\n",
    "    labels = data[1]\n",
    "    test_images.append(images)\n",
    "    test_labels.append(labels)\n",
    "    pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(),\n",
    "                            axis=1)\n",
    "    predict_labels.append(pred_labels)\n",
    "    if i >= batch_num:\n",
    "        break\n",
    "predict_labels = np.concatenate(predict_labels)\n",
    "true_labels = np.argmax(np.concatenate(test_labels), axis=1)\n",
    "accuracy = np.mean(np.equal(predict_labels, true_labels))\n",
    "LOGGER.info(TAG, \"prediction accuracy before attacking is : %s\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运行以下一段代码，打印测试数据图像信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test images before attacking showed below:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1200 with 96 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(100.0, 120.0), dpi=10)\n",
    "for j,images in enumerate(test_images):\n",
    "    for i,image in enumerate(images):\n",
    "        plt.subplot(12, 8, i+1+32*j)\n",
    "        plt.subplots_adjust(wspace=0.3)\n",
    "        image = np.squeeze(image, 0)\n",
    "        image = image/np.amax(image)\n",
    "        image = np.clip(image, 0, 1)\n",
    "        plt.imshow(image, cmap='gray', interpolation='nearest')\n",
    "        # plt.title(f\"image {i+1}\\nlabel:  {batch_label[i]}\", y=-0.65, fontdict={\"fontsize\":8})\n",
    "        plt.axis('off')   \n",
    "print(\"Test images before attacking showed below:\\n\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 攻击模型\n",
    "\n",
    "调用MindArmour提供的FGSM接口（`FastGradientSignMethod`），使用被攻击前抽取的96张数据图像`test_images`作为被攻击数据集，保存被攻击后数据集图像到当前notebook目录下的`ada_data`文件中。其中，参数`eps`为攻击对数据范围产生的单步对抗性摄动的比例，该值越大，则攻击程度越大。关于`FastGradientSignMethod`的详细使用说明，可参考[官方API文档](https://www.mindspore.cn/api/zh-CN/master/api/python/mindarmour/mindarmour.attacks.html?highlight=fastgradientsignmethod#mindarmour.attacks.FastGradientSignMethod)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from mindarmour.attacks.gradient_method import FastGradientSignMethod\n",
    "\n",
    "\n",
    "# attacking\n",
    "attack = FastGradientSignMethod(net, eps=0.3)\n",
    "start_time = time.perf_counter()\n",
    "adv_data = attack.batch_generate(np.concatenate(test_images),\n",
    "                                 np.concatenate(test_labels), batch_size=32)\n",
    "stop_time = time.perf_counter()\n",
    "np.save('./adv_data', adv_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试攻击后模型精度\n",
    "\n",
    "对模型进行FGSM无目标攻击后，通过输出信息可以看到：\n",
    "\n",
    "模型精度由97.9%降到19.8%，误分类率高达80%，成功攻击的对抗样本的预测类别的平均置信度（ACAC）为 0.73765075，成功攻击的对抗样本的真实类别的平均置信度（ACTC）为 0.041447524，同时给出了生成的对抗样本与原始样本的零范数距离、二范数距离和无穷范数距离，平均每个对抗样本与原始样本间的结构相似性为0.33272708，平均每生成一张对抗样本所需时间为0.001377s。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:29,568 [<ipython-input-11-7db9d04380f1>:10] [demo] prediction accuracy after attacking is : 0.19791666666666666\n",
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:29,573 [<ipython-input-11-7db9d04380f1>:17] [demo] mis-classification rate of adversaries is : 0.8020833333333334\n",
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:29,575 [<ipython-input-11-7db9d04380f1>:19] [demo] The average confidence of adversarial class is : 0.73765075\n",
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:29,576 [<ipython-input-11-7db9d04380f1>:21] [demo] The average confidence of true class is : 0.041447524\n",
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:29,583 [<ipython-input-11-7db9d04380f1>:24] [demo] The average distance (l0, l2, linf) between original samples and adversarial samples are: (1.6081334162994763, 0.45166983136190164, 0.3000000374657734)\n",
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:29,784 [<ipython-input-11-7db9d04380f1>:27] [demo] The average structural similarity between original samples and adversarial samples are: 0.3327270836541148\n",
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:29,787 [<ipython-input-11-7db9d04380f1>:29] [demo] The average costing time is 0.0013771560212868887\n"
     ]
    }
   ],
   "source": [
    "from scipy.special import softmax\n",
    "from mindarmour.evaluations.attack_evaluation import AttackEvaluate\n",
    "\n",
    "\n",
    "pred_logits_adv = model.predict(Tensor(adv_data)).asnumpy()\n",
    "# rescale predict confidences into (0, 1).\n",
    "pred_logits_adv = softmax(pred_logits_adv, axis=1)\n",
    "pred_labels_adv = np.argmax(pred_logits_adv, axis=1)\n",
    "accuracy_adv = np.mean(np.equal(pred_labels_adv, true_labels))\n",
    "LOGGER.info(TAG, \"prediction accuracy after attacking is : %s\", accuracy_adv)\n",
    "attack_evaluate = AttackEvaluate(np.concatenate(test_images).transpose(0, 2, 3, 1),\n",
    "                                 np.concatenate(test_labels),\n",
    "                                 adv_data.transpose(0, 2, 3, 1),\n",
    "                                 pred_logits_adv)\n",
    "\n",
    "LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',\n",
    "            attack_evaluate.mis_classification_rate())\n",
    "LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',\n",
    "            attack_evaluate.avg_conf_adv_class())\n",
    "LOGGER.info(TAG, 'The average confidence of true class is : %s',\n",
    "            attack_evaluate.avg_conf_true_class())\n",
    "LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '\n",
    "            'samples and adversarial samples are: %s',\n",
    "            attack_evaluate.avg_lp_distance())\n",
    "LOGGER.info(TAG, 'The average structural similarity between original '\n",
    "            'samples and adversarial samples are: %s',\n",
    "            attack_evaluate.avg_ssim())\n",
    "LOGGER.info(TAG, 'The average costing time is %s',\n",
    "            (stop_time - start_time)/(batch_num*batch_size))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印被攻击后的测试图像信息，由输出图像结果可以看出，被攻击后的图像和攻击前相比，成功误导了模型，使模型将其误分类为其他非正确类别。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test images after attacking showed below:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1200 with 96 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(100.0, 120.0), dpi=10)\n",
    "plt.set_cmap('binary')\n",
    "for i,image in enumerate(adv_data):\n",
    "    plt.subplot(12, 8, i+1)\n",
    "    # plt.subplots_adjust(wspace=0.2, hspace=0.2)\n",
    "    image = np.squeeze(image, 0)\n",
    "    image = image/np.amax(image)\n",
    "    image = np.clip(image, 0, 1)\n",
    "    plt.imshow(image, cmap='gray', interpolation='nearest')\n",
    "    plt.axis('off')   \n",
    "print(\"Test images after attacking showed below:\\n\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对抗性防御\n",
    "\n",
    "`NaturalAdversarialDefense`（NAD）是一种简单有效的对抗样本防御方法，使用对抗训练的方式，在模型训练的过程中构建对抗样本，并将对抗样本与原始样本混合，一起训练模型。随着训练次数的增加，模型在训练的过程中提升对于对抗样本的鲁棒性。NAD算法使用FGSM作为攻击算法，构建对抗样本。\n",
    "\n",
    "### 防御实现\n",
    "\n",
    "调用MindArmour提供的NAD防御接口（`NaturalAdversarialDefense`）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:35,859 [<ipython-input-13-f8a913e8fc63>:29] [demo] accuracy of TEST data on defensed model is : 1.0\n",
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:35,876 [<ipython-input-13-f8a913e8fc63>:44] [demo] accuracy of adv data on defensed model is : 0.875\n",
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:35,878 [<ipython-input-13-f8a913e8fc63>:46] [demo] defense mis-classification rate of adversaries is : 0.0\n",
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:35,880 [<ipython-input-13-f8a913e8fc63>:48] [demo] The average confidence of adversarial class is : 0\n",
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:35,881 [<ipython-input-13-f8a913e8fc63>:50] [demo] The average confidence of true class is : 0\n",
      "[INFO] MA(23555:139900077446976,MainProcess):2020-08-08 14:51:35,883 [<ipython-input-13-f8a913e8fc63>:53] [demo] The average distance (l0, l2, linf) between original samples and adversarial samples are: (-1, -1, -1)\n"
     ]
    }
   ],
   "source": [
    "from mindspore.nn import SoftmaxCrossEntropyWithLogits\n",
    "from mindarmour.defenses import NaturalAdversarialDefense\n",
    "\n",
    "\n",
    "loss = SoftmaxCrossEntropyWithLogits(sparse=False, reduction='mean')\n",
    "opt = nn.Momentum(net.trainable_params(), 0.01, 0.09)\n",
    "\n",
    "nad = NaturalAdversarialDefense(net, loss_fn=loss, optimizer=opt,\n",
    "                                bounds=(0.0, 1.0), eps=0.3)\n",
    "net.set_train()\n",
    "nad.batch_defense(np.concatenate(test_images), np.concatenate(test_labels),\n",
    "                  batch_size=32, epochs=20)\n",
    "\n",
    "# get accuracy of test data on defensed model\n",
    "net.set_train(False)\n",
    "acc_list = []\n",
    "pred_logits_adv = []\n",
    "for i in range(batch_num):\n",
    "    batch_inputs = test_images[i]\n",
    "    batch_labels = test_labels[i]\n",
    "    logits = net(Tensor(batch_inputs)).asnumpy()\n",
    "    pred_logits_adv.append(logits)\n",
    "    label_pred = np.argmax(logits, axis=1)\n",
    "    acc_list.append(np.mean(np.argmax(batch_labels, axis=1) == label_pred))\n",
    "pred_logits_adv = np.concatenate(pred_logits_adv)\n",
    "pred_logits_adv = softmax(pred_logits_adv, axis=1)\n",
    "\n",
    "LOGGER.info(TAG, 'accuracy of TEST data on defensed model is : %s',\n",
    "             np.mean(acc_list))\n",
    "acc_list = []\n",
    "for i in range(batch_num):\n",
    "    batch_inputs = adv_data[i * batch_size: (i + 1) * batch_size]\n",
    "    batch_labels = test_labels[i]\n",
    "    logits = net(Tensor(batch_inputs)).asnumpy()\n",
    "    label_pred = np.argmax(logits, axis=1)\n",
    "    acc_list.append(np.mean(np.argmax(batch_labels, axis=1) == label_pred))\n",
    "\n",
    "attack_evaluate = AttackEvaluate(np.concatenate(test_images),\n",
    "                                 np.concatenate(test_labels),\n",
    "                                 adv_data,\n",
    "                                 pred_logits_adv)\n",
    "\n",
    "LOGGER.info(TAG, 'accuracy of adv data on defensed model is : %s',\n",
    "            np.mean(acc_list))\n",
    "LOGGER.info(TAG, 'defense mis-classification rate of adversaries is : %s',\n",
    "            attack_evaluate.mis_classification_rate())\n",
    "LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',\n",
    "            attack_evaluate.avg_conf_adv_class())\n",
    "LOGGER.info(TAG, 'The average confidence of true class is : %s',\n",
    "            attack_evaluate.avg_conf_true_class())\n",
    "LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '\n",
    "            'samples and adversarial samples are: %s',\n",
    "            attack_evaluate.avg_lp_distance())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 防御效果\n",
    "\n",
    "通过输出信息可以看出，使用NAD进行对抗样本防御后，模型对于对抗样本的误分类率从80%降至12.5%，模型有效地防御了对抗样本。同时，模型对于原来测试数据集的分类精度达100%，使用NAD防御功能，并未降低模型的分类精度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "\n",
    "以上便完成了MindArmour在模型安全上的应用体验，我们通过本次体验理解了对模型攻击的概念和原理，了解了如何使用`FastGradientSignMethod`接口对模型攻击和`NaturalAdversarialDefense`接口实现对抗性防御。"
   ]
  }
 ],
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